def persist(self, path: Text) -> None:
"""Persists the policy to a storage."""
if self.session is None:
logger.debug("Method `persist(...)` was called "
"without a trained model present. "
"Nothing to persist then!")
return
self.featurizer.persist(path)
meta = {
"priority": self.priority,
"loss_type": self.loss_type,
"ranking_length": self.ranking_length,
}
meta_file = os.path.join(path, "embedding_policy.json")
rasa.utils.io.dump_obj_as_json_to_file(meta_file, meta)
file_name = "tensorflow_embedding.ckpt"
checkpoint = os.path.join(path, file_name)
rasa.utils.io.create_directory_for_file(checkpoint)
with self.graph.as_default():
train_utils.persist_tensor("user_placeholder", self.a_in,
self.graph)
train_utils.persist_tensor("bot_placeholder", self.b_in,
self.graph)
train_utils.persist_tensor("similarity_all", self.sim_all,
self.graph)
train_utils.persist_tensor("pred_confidence", self.pred_confidence,
self.graph)
train_utils.persist_tensor("similarity", self.sim, self.graph)
train_utils.persist_tensor("dial_embed", self.dial_embed,
self.graph)
train_utils.persist_tensor("bot_embed", self.bot_embed, self.graph)
train_utils.persist_tensor("all_bot_embed", self.all_bot_embed,
self.graph)
train_utils.persist_tensor("attention_weights",
self.attention_weights, self.graph)
saver = tf.train.Saver()
saver.save(self.session, checkpoint)
with open(os.path.join(path, file_name + ".tf_config.pkl"), "wb") as f:
pickle.dump(self._tf_config, f)
def persist(self, file_name: Text, model_dir: Text) -> Dict[Text, Any]:
"""Persist this model into the passed directory.
Return the metadata necessary to load the model again.
"""
if self.session is None:
return {"file": None}
checkpoint = os.path.join(model_dir, file_name + ".ckpt")
try:
os.makedirs(os.path.dirname(checkpoint))
except OSError as e:
# be happy if someone already created the path
import errno
if e.errno != errno.EEXIST:
raise
with self.graph.as_default():
train_utils.persist_tensor("batch_placeholder", self.batch_in,
self.graph)
train_utils.persist_tensor("similarity_all", self.sim_all,
self.graph)
train_utils.persist_tensor("pred_confidence", self.pred_confidence,
self.graph)
train_utils.persist_tensor("similarity", self.sim, self.graph)
train_utils.persist_tensor("message_embed", self.message_embed,
self.graph)
train_utils.persist_tensor("label_embed", self.label_embed,
self.graph)
train_utils.persist_tensor("all_labels_embed",
self.all_labels_embed, self.graph)
saver = tf.train.Saver()
saver.save(self.session, checkpoint)
with open(os.path.join(model_dir, file_name + ".inv_label_dict.pkl"),
"wb") as f:
pickle.dump(self.inverted_label_dict, f)
with open(os.path.join(model_dir, file_name + ".tf_config.pkl"),
"wb") as f:
pickle.dump(self._tf_config, f)
with open(
os.path.join(model_dir, file_name + ".batch_tuple_sizes.pkl"),
"wb") as f:
pickle.dump(self.batch_tuple_sizes, f)
return {"file": file_name}
